Overview

Dataset statistics

Number of variables23
Number of observations8327
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory184.0 B

Variable types

Categorical5
Numeric18

Warnings

mean_revenue_level_event has constant value "0.0" Constant
std_revenue_level_event has constant value "0.0" Constant
userID_anonymized has a high cardinality: 8327 distinct values High cardinality
model has a high cardinality: 78 distinct values High cardinality
operating_system has a high cardinality: 53 distinct values High cardinality
total_count_banner_impression is highly correlated with total_count_interstitial_impressionHigh correlation
total_count_interstitial_impression is highly correlated with total_count_banner_impressionHigh correlation
last_time_banner_impression is highly correlated with last_time_level_eventHigh correlation
last_time_level_event is highly correlated with last_time_banner_impressionHigh correlation
mean_revenue_level_event is highly correlated with operating_system and 2 other fieldsHigh correlation
operating_system is highly correlated with mean_revenue_level_event and 1 other fieldsHigh correlation
std_revenue_level_event is highly correlated with mean_revenue_level_event and 2 other fieldsHigh correlation
model is highly correlated with mean_revenue_level_event and 1 other fieldsHigh correlation
userID_anonymized is uniformly distributed Uniform
userID_anonymized has unique values Unique
mean_revenue_interstitial_impression has 471 (5.7%) zeros Zeros
mean_revenue_rewarded_impression has 6101 (73.3%) zeros Zeros
std_revenue_banner_impression has 1412 (17.0%) zeros Zeros
std_revenue_interstitial_impression has 1929 (23.2%) zeros Zeros
std_revenue_rewarded_impression has 7602 (91.3%) zeros Zeros
total_count_interstitial_impression has 471 (5.7%) zeros Zeros
total_count_rewarded_impression has 6101 (73.3%) zeros Zeros
last_time_banner_impression has 203 (2.4%) zeros Zeros
last_time_interstitial_impression has 513 (6.2%) zeros Zeros
last_time_level_event has 172 (2.1%) zeros Zeros
last_time_rewarded_impression has 6101 (73.3%) zeros Zeros
mean_wifi_state has 1181 (14.2%) zeros Zeros

Reproduction

Analysis started2021-03-31 10:40:29.468610
Analysis finished2021-03-31 10:41:01.492351
Duration32.02 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

userID_anonymized
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct8327
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
b8fc3ca1-851a
 
1
7047c321-6372
 
1
9f459220-15f9
 
1
3b28b962-e4ef
 
1
dfea96f0-9481
 
1
Other values (8322)
8322 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters108251
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8327 ?
Unique (%)100.0%

Sample

1st row00081f8b-253d
2nd row000ab112-2616
3rd row002ff54d-8749
4th row0030d0af-2830
5th row00441340-5d9c
ValueCountFrequency (%)
b8fc3ca1-851a1
 
< 0.1%
7047c321-63721
 
< 0.1%
9f459220-15f91
 
< 0.1%
3b28b962-e4ef1
 
< 0.1%
dfea96f0-94811
 
< 0.1%
bbf2611f-240e1
 
< 0.1%
1ae2129e-cd901
 
< 0.1%
550e803e-01851
 
< 0.1%
777089ec-4abc1
 
< 0.1%
92737164-936b1
 
< 0.1%
Other values (8317)8317
99.9%
2021-03-31T13:41:01.676901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b8fc3ca1-851a1
 
< 0.1%
7047c321-63721
 
< 0.1%
9f459220-15f91
 
< 0.1%
3b28b962-e4ef1
 
< 0.1%
dfea96f0-94811
 
< 0.1%
bbf2611f-240e1
 
< 0.1%
1ae2129e-cd901
 
< 0.1%
550e803e-01851
 
< 0.1%
777089ec-4abc1
 
< 0.1%
92737164-936b1
 
< 0.1%
Other values (8317)8317
99.9%

Most occurring characters

ValueCountFrequency (%)
-8327
 
7.7%
c6354
 
5.9%
f6334
 
5.9%
d6328
 
5.8%
96323
 
5.8%
26287
 
5.8%
56278
 
5.8%
e6274
 
5.8%
36242
 
5.8%
a6232
 
5.8%
Other values (7)43272
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number62173
57.4%
Lowercase Letter37751
34.9%
Dash Punctuation8327
 
7.7%

Most frequent character per category

ValueCountFrequency (%)
96323
10.2%
26287
10.1%
56278
10.1%
36242
10.0%
76223
10.0%
46196
10.0%
06194
10.0%
66174
9.9%
86158
9.9%
16098
9.8%
ValueCountFrequency (%)
c6354
16.8%
f6334
16.8%
d6328
16.8%
e6274
16.6%
a6232
16.5%
b6229
16.5%
ValueCountFrequency (%)
-8327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common70500
65.1%
Latin37751
34.9%

Most frequent character per script

ValueCountFrequency (%)
-8327
11.8%
96323
9.0%
26287
8.9%
56278
8.9%
36242
8.9%
76223
8.8%
46196
8.8%
06194
8.8%
66174
8.8%
86158
8.7%
ValueCountFrequency (%)
c6354
16.8%
f6334
16.8%
d6328
16.8%
e6274
16.6%
a6232
16.5%
b6229
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII108251
100.0%

Most frequent character per block

ValueCountFrequency (%)
-8327
 
7.7%
c6354
 
5.9%
f6334
 
5.9%
d6328
 
5.8%
96323
 
5.8%
26287
 
5.8%
56278
 
5.8%
e6274
 
5.8%
36242
 
5.8%
a6232
 
5.8%
Other values (7)43272
40.0%

mean_revenue_banner_impression
Real number (ℝ≥0)

Distinct5936
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005221691362
Minimum0
Maximum0.01
Zeros26
Zeros (%)0.3%
Memory size65.2 KiB
2021-03-31T13:41:01.772566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000206335
Q10.0003712757727
median0.0004341166667
Q30.0006
95-th percentile0.001056578947
Maximum0.01
Range0.01
Interquartile range (IQR)0.0002287242273

Descriptive statistics

Standard deviation0.0003730829112
Coefficient of variation (CV)0.7144867157
Kurtosis134.5641549
Mean0.0005221691362
Median Absolute Deviation (MAD)0.0001041452381
Skewness8.022347211
Sum4.348102397
Variance1.391908586 × 107
MonotocityNot monotonic
2021-03-31T13:41:01.895730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0006471
 
5.7%
0.0004372
 
4.5%
0.0004183
 
2.2%
0.0004172
 
2.1%
0.000542
 
0.5%
0.000334
 
0.4%
0.000827
 
0.3%
026
 
0.3%
0.001425
 
0.3%
0.00124
 
0.3%
Other values (5926)6951
83.5%
ValueCountFrequency (%)
026
0.3%
4.60483871 × 1051
 
< 0.1%
5 × 1051
 
< 0.1%
5.125405405 × 1051
 
< 0.1%
6.352941176 × 1051
 
< 0.1%
ValueCountFrequency (%)
0.011
< 0.1%
0.0095647111111
< 0.1%
0.0073521
< 0.1%
0.0061193333331
< 0.1%
0.006041
< 0.1%

mean_revenue_interstitial_impression
Real number (ℝ≥0)

ZEROS

Distinct3108
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02668262276
Minimum0
Maximum0.1825985
Zeros471
Zeros (%)5.7%
Memory size65.2 KiB
2021-03-31T13:41:02.024022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02239208075
median0.0275
Q30.0309375
95-th percentile0.0425
Maximum0.1825985
Range0.1825985
Interquartile range (IQR)0.008545419255

Descriptive statistics

Standard deviation0.01034003777
Coefficient of variation (CV)0.3875195427
Kurtosis10.75169327
Mean0.02668262276
Median Absolute Deviation (MAD)0.004310810811
Skewness0.3714099411
Sum222.1861997
Variance0.0001069163811
MonotocityNot monotonic
2021-03-31T13:41:02.143357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03952
 
11.4%
0471
 
5.7%
0.035281
 
3.4%
0.0275237
 
2.8%
0.025189
 
2.3%
0.0325146
 
1.8%
0.05121
 
1.5%
0.04115
 
1.4%
0.03166666667108
 
1.3%
0.02625106
 
1.3%
Other values (3098)5601
67.3%
ValueCountFrequency (%)
0471
5.7%
0.0011093751
 
< 0.1%
0.001150451
 
< 0.1%
0.0042
 
< 0.1%
0.0056666666671
 
< 0.1%
ValueCountFrequency (%)
0.18259851
< 0.1%
0.150051
< 0.1%
0.097896430071
< 0.1%
0.0900500032
< 0.1%
0.0800500031
< 0.1%

mean_revenue_level_event
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
0.0
8327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24981
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08327
100.0%
2021-03-31T13:41:02.320748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-31T13:41:02.376416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.08327
100.0%

Most occurring characters

ValueCountFrequency (%)
016654
66.7%
.8327
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16654
66.7%
Other Punctuation8327
33.3%

Most frequent character per category

ValueCountFrequency (%)
016654
100.0%
ValueCountFrequency (%)
.8327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24981
100.0%

Most frequent character per script

ValueCountFrequency (%)
016654
66.7%
.8327
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24981
100.0%

Most frequent character per block

ValueCountFrequency (%)
016654
66.7%
.8327
33.3%

mean_revenue_rewarded_impression
Real number (ℝ≥0)

ZEROS

Distinct357
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00975634518
Minimum0
Maximum0.1
Zeros6101
Zeros (%)73.3%
Memory size65.2 KiB
2021-03-31T13:41:02.444571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.018
95-th percentile0.04166666667
Maximum0.1
Range0.1
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.01750974335
Coefficient of variation (CV)1.794703142
Kurtosis2.10176462
Mean0.00975634518
Median Absolute Deviation (MAD)0
Skewness1.652640363
Sum81.24108631
Variance0.0003065911122
MonotocityNot monotonic
2021-03-31T13:41:02.558780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06101
73.3%
0.04883
 
10.6%
0.045181
 
2.2%
0.01897
 
1.2%
0.027569
 
0.8%
0.0268
 
0.8%
0.0849
 
0.6%
0.042544
 
0.5%
0.022543
 
0.5%
0.02540
 
0.5%
Other values (347)752
 
9.0%
ValueCountFrequency (%)
06101
73.3%
0.00453561
 
< 0.1%
0.014
 
< 0.1%
0.0100321
 
< 0.1%
0.011333333331
 
< 0.1%
ValueCountFrequency (%)
0.110
 
0.1%
0.0951
 
< 0.1%
0.091
 
< 0.1%
0.094
 
< 0.1%
0.0849
0.6%

std_revenue_banner_impression
Real number (ℝ≥0)

ZEROS

Distinct6465
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0002298932863
Minimum0
Maximum0.006717514421
Zeros1412
Zeros (%)17.0%
Memory size65.2 KiB
2021-03-31T13:41:02.658405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.472135955 × 105
median0.0001162419757
Q30.0002514402955
95-th percentile0.0007779328258
Maximum0.006717514421
Range0.006717514421
Interquartile range (IQR)0.000206718936

Descriptive statistics

Standard deviation0.0003986040765
Coefficient of variation (CV)1.733865668
Kurtosis44.86250919
Mean0.0002298932863
Median Absolute Deviation (MAD)8.88596428 × 105
Skewness5.433065532
Sum1.914321395
Variance1.588852098 × 107
MonotocityNot monotonic
2021-03-31T13:41:02.753990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01412
 
17.0%
0.000410
 
0.1%
0.00042426406879
 
0.1%
5.773502692 × 1059
 
0.1%
9.660917831 × 1058
 
0.1%
0.00017888543828
 
0.1%
2.886751346 × 1058
 
0.1%
0.00014142135627
 
0.1%
4.472135955 × 1057
 
0.1%
0.000557
 
0.1%
Other values (6455)6842
82.2%
ValueCountFrequency (%)
01412
17.0%
2.683281573 × 1071
 
< 0.1%
2.752988806 × 1071
 
< 0.1%
3 × 1071
 
< 0.1%
3.207134903 × 1071
 
< 0.1%
ValueCountFrequency (%)
0.0067175144211
< 0.1%
0.0062874950661
< 0.1%
0.0057193688391
< 0.1%
0.0045467160821
< 0.1%
0.004338930091
< 0.1%

std_revenue_interstitial_impression
Real number (ℝ≥0)

ZEROS

Distinct4188
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004708151302
Minimum0
Maximum0.08108329824
Zeros1929
Zeros (%)23.2%
Memory size65.2 KiB
2021-03-31T13:41:02.864143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002041241452
median0.004082482905
Q30.006531089102
95-th percentile0.01192581549
Maximum0.08108329824
Range0.08108329824
Interquartile range (IQR)0.004489847649

Descriptive statistics

Standard deviation0.004506547278
Coefficient of variation (CV)0.9571797907
Kurtosis25.52887288
Mean0.004708151302
Median Absolute Deviation (MAD)0.002314715952
Skewness2.937585233
Sum39.20477589
Variance2.030896837 × 105
MonotocityNot monotonic
2021-03-31T13:41:02.971635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01929
 
23.2%
0.003535533906111
 
1.3%
0.003535533906105
 
1.3%
0.00288675134689
 
1.1%
0.002580
 
1.0%
0.00288675134680
 
1.0%
0.00288675134658
 
0.7%
0.0115470053851
 
0.6%
0.00543
 
0.5%
0.00288675134643
 
0.5%
Other values (4178)5738
68.9%
ValueCountFrequency (%)
01929
23.2%
1.767731598 × 1051
 
< 0.1%
2.041200627 × 1051
 
< 0.1%
2.041200627 × 1051
 
< 0.1%
2.236023256 × 1051
 
< 0.1%
ValueCountFrequency (%)
0.081083298241
< 0.1%
0.069310899821
< 0.1%
0.05418487451
< 0.1%
0.049532832141
< 0.1%
0.046508643591
< 0.1%

std_revenue_level_event
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
0.0
8327 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24981
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.08327
100.0%
2021-03-31T13:41:03.140863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-03-31T13:41:03.189247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.08327
100.0%

Most occurring characters

ValueCountFrequency (%)
016654
66.7%
.8327
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16654
66.7%
Other Punctuation8327
33.3%

Most frequent character per category

ValueCountFrequency (%)
016654
100.0%
ValueCountFrequency (%)
.8327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common24981
100.0%

Most frequent character per script

ValueCountFrequency (%)
016654
66.7%
.8327
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII24981
100.0%

Most frequent character per block

ValueCountFrequency (%)
016654
66.7%
.8327
33.3%

std_revenue_rewarded_impression
Real number (ℝ≥0)

ZEROS

Distinct413
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0009224826137
Minimum0
Maximum0.04725815626
Zeros7602
Zeros (%)91.3%
Memory size65.2 KiB
2021-03-31T13:41:03.243034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.008838834765
Maximum0.04725815626
Range0.04725815626
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.003617943468
Coefficient of variation (CV)3.921963856
Kurtosis34.34179032
Mean0.0009224826137
Median Absolute Deviation (MAD)0
Skewness5.22593711
Sum7.681512724
Variance1.308951494 × 105
MonotocityNot monotonic
2021-03-31T13:41:03.340744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07602
91.3%
0.0155563491929
 
0.3%
0.00353553390629
 
0.3%
0.00353553390624
 
0.3%
0.0106066017223
 
0.3%
0.0123743686722
 
0.3%
0.00883883476521
 
0.3%
0.00883883476516
 
0.2%
0.0141421356210
 
0.1%
0.012701705929
 
0.1%
Other values (403)542
 
6.5%
ValueCountFrequency (%)
07602
91.3%
2.886693611 × 1051
 
< 0.1%
4.5254834 × 1051
 
< 0.1%
0.00011316065281
 
< 0.1%
0.00070710678121
 
< 0.1%
ValueCountFrequency (%)
0.047258156261
< 0.1%
0.042426406872
< 0.1%
0.038890872972
< 0.1%
0.03752776751
< 0.1%
0.037444403231
< 0.1%

total_count_banner_impression
Real number (ℝ≥0)

HIGH CORRELATION

Distinct277
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.72727273
Minimum0
Maximum596
Zeros26
Zeros (%)0.3%
Memory size65.2 KiB
2021-03-31T13:41:03.438122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median21
Q346
95-th percentile128
Maximum596
Range596
Interquartile range (IQR)37

Descriptive statistics

Standard deviation46.26717681
Coefficient of variation (CV)1.259749864
Kurtosis16.36676802
Mean36.72727273
Median Absolute Deviation (MAD)14
Skewness3.216623721
Sum305828
Variance2140.65165
MonotocityNot monotonic
2021-03-31T13:41:03.530574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7270
 
3.2%
4268
 
3.2%
10246
 
3.0%
6244
 
2.9%
5243
 
2.9%
2236
 
2.8%
8235
 
2.8%
12215
 
2.6%
1214
 
2.6%
13206
 
2.5%
Other values (267)5950
71.5%
ValueCountFrequency (%)
026
 
0.3%
1214
2.6%
2236
2.8%
3197
2.4%
4268
3.2%
ValueCountFrequency (%)
5961
< 0.1%
5071
< 0.1%
4691
< 0.1%
4551
< 0.1%
4511
< 0.1%

total_count_interstitial_impression
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct110
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.37024138
Minimum0
Maximum212
Zeros471
Zeros (%)5.7%
Memory size65.2 KiB
2021-03-31T13:41:03.630430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q312
95-th percentile37
Maximum212
Range212
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.91063732
Coefficient of variation (CV)1.341399569
Kurtosis26.06580814
Mean10.37024138
Median Absolute Deviation (MAD)4
Skewness3.900920541
Sum86353
Variance193.5058306
MonotocityNot monotonic
2021-03-31T13:41:03.724133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2771
 
9.3%
1760
 
9.1%
3741
 
8.9%
4676
 
8.1%
5586
 
7.0%
6475
 
5.7%
0471
 
5.7%
7403
 
4.8%
8383
 
4.6%
9314
 
3.8%
Other values (100)2747
33.0%
ValueCountFrequency (%)
0471
5.7%
1760
9.1%
2771
9.3%
3741
8.9%
4676
8.1%
ValueCountFrequency (%)
2121
< 0.1%
1961
< 0.1%
1611
< 0.1%
1531
< 0.1%
1392
< 0.1%

total_count_level_event
Real number (ℝ≥0)

Distinct234
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.15647892
Minimum0
Maximum666
Zeros16
Zeros (%)0.2%
Memory size65.2 KiB
2021-03-31T13:41:04.016925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q111
median19
Q339
95-th percentile103
Maximum666
Range666
Interquartile range (IQR)28

Descriptive statistics

Standard deviation37.39019653
Coefficient of variation (CV)1.162757795
Kurtosis28.96861086
Mean32.15647892
Median Absolute Deviation (MAD)11
Skewness3.806947495
Sum267767
Variance1398.026797
MonotocityNot monotonic
2021-03-31T13:41:04.109675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15418
 
5.0%
9283
 
3.4%
16251
 
3.0%
3250
 
3.0%
12249
 
3.0%
13245
 
2.9%
5244
 
2.9%
8232
 
2.8%
11231
 
2.8%
7231
 
2.8%
Other values (224)5693
68.4%
ValueCountFrequency (%)
016
 
0.2%
166
 
0.8%
2128
1.5%
3250
3.0%
481
 
1.0%
ValueCountFrequency (%)
6661
< 0.1%
6091
< 0.1%
3981
< 0.1%
3941
< 0.1%
3571
< 0.1%

total_count_rewarded_impression
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5429326288
Minimum0
Maximum32
Zeros6101
Zeros (%)73.3%
Memory size65.2 KiB
2021-03-31T13:41:04.202220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum32
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.483531244
Coefficient of variation (CV)2.732440758
Kurtosis71.76080173
Mean0.5429326288
Median Absolute Deviation (MAD)0
Skewness6.590243624
Sum4521
Variance2.200864951
MonotocityNot monotonic
2021-03-31T13:41:04.278224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
06101
73.3%
11408
 
16.9%
2380
 
4.6%
3156
 
1.9%
481
 
1.0%
564
 
0.8%
639
 
0.5%
826
 
0.3%
721
 
0.3%
914
 
0.2%
Other values (14)37
 
0.4%
ValueCountFrequency (%)
06101
73.3%
11408
 
16.9%
2380
 
4.6%
3156
 
1.9%
481
 
1.0%
ValueCountFrequency (%)
321
< 0.1%
241
< 0.1%
221
< 0.1%
211
< 0.1%
201
< 0.1%

last_time_banner_impression
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3224
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1587.088147
Minimum0
Maximum5759
Zeros203
Zeros (%)2.4%
Memory size65.2 KiB
2021-03-31T13:41:04.369918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median801
Q32934
95-th percentile5350.7
Maximum5759
Range5759
Interquartile range (IQR)2927

Descriptive statistics

Standard deviation1847.378872
Coefficient of variation (CV)1.16400521
Kurtosis-0.6633287444
Mean1587.088147
Median Absolute Deviation (MAD)798
Skewness0.8567764343
Sum13215683
Variance3412808.697
MonotocityNot monotonic
2021-03-31T13:41:04.464944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2394
 
4.7%
1348
 
4.2%
3321
 
3.9%
4280
 
3.4%
5254
 
3.1%
0203
 
2.4%
7160
 
1.9%
6154
 
1.8%
8116
 
1.4%
9103
 
1.2%
Other values (3214)5994
72.0%
ValueCountFrequency (%)
0203
2.4%
1348
4.2%
2394
4.7%
3321
3.9%
4280
3.4%
ValueCountFrequency (%)
57595
0.1%
57584
< 0.1%
57572
 
< 0.1%
57561
 
< 0.1%
57542
 
< 0.1%

last_time_interstitial_impression
Real number (ℝ≥0)

ZEROS

Distinct2990
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1322.423802
Minimum0
Maximum5759
Zeros513
Zeros (%)6.2%
Memory size65.2 KiB
2021-03-31T13:41:04.567837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median209
Q32496
95-th percentile5070.3
Maximum5759
Range5759
Interquartile range (IQR)2492

Descriptive statistics

Standard deviation1743.743025
Coefficient of variation (CV)1.318596219
Kurtosis-0.07015763026
Mean1322.423802
Median Absolute Deviation (MAD)209
Skewness1.122802386
Sum11011823
Variance3040639.737
MonotocityNot monotonic
2021-03-31T13:41:04.664426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0513
 
6.2%
1470
 
5.6%
2454
 
5.5%
3376
 
4.5%
4341
 
4.1%
5225
 
2.7%
6179
 
2.1%
7163
 
2.0%
8124
 
1.5%
9104
 
1.2%
Other values (2980)5378
64.6%
ValueCountFrequency (%)
0513
6.2%
1470
5.6%
2454
5.5%
3376
4.5%
4341
4.1%
ValueCountFrequency (%)
57593
< 0.1%
57582
< 0.1%
57573
< 0.1%
57561
 
< 0.1%
57551
 
< 0.1%

last_time_level_event
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3227
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1587.319923
Minimum0
Maximum5759
Zeros172
Zeros (%)2.1%
Memory size65.2 KiB
2021-03-31T13:41:04.765895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median794
Q32930
95-th percentile5350
Maximum5759
Range5759
Interquartile range (IQR)2923

Descriptive statistics

Standard deviation1848.971685
Coefficient of variation (CV)1.164838706
Kurtosis-0.663072578
Mean1587.319923
Median Absolute Deviation (MAD)791
Skewness0.858076271
Sum13217613
Variance3418696.29
MonotocityNot monotonic
2021-03-31T13:41:04.876878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2407
 
4.9%
1366
 
4.4%
3345
 
4.1%
4284
 
3.4%
5261
 
3.1%
6174
 
2.1%
0172
 
2.1%
7151
 
1.8%
9112
 
1.3%
8111
 
1.3%
Other values (3217)5944
71.4%
ValueCountFrequency (%)
0172
2.1%
1366
4.4%
2407
4.9%
3345
4.1%
4284
3.4%
ValueCountFrequency (%)
57596
0.1%
57584
< 0.1%
57572
 
< 0.1%
57561
 
< 0.1%
57542
 
< 0.1%

last_time_rewarded_impression
Real number (ℝ≥0)

ZEROS

Distinct1099
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.3716825
Minimum0
Maximum5751
Zeros6101
Zeros (%)73.3%
Memory size65.2 KiB
2021-03-31T13:41:04.999925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile2101.2
Maximum5751
Range5751
Interquartile range (IQR)5

Descriptive statistics

Standard deviation881.5745591
Coefficient of variation (CV)3.236660107
Kurtosis16.03154891
Mean272.3716825
Median Absolute Deviation (MAD)0
Skewness3.939369778
Sum2268039
Variance777173.7032
MonotocityNot monotonic
2021-03-31T13:41:05.104015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06101
73.3%
6155
 
1.9%
5151
 
1.8%
798
 
1.2%
472
 
0.9%
872
 
0.9%
1045
 
0.5%
940
 
0.5%
1230
 
0.4%
1327
 
0.3%
Other values (1089)1536
 
18.4%
ValueCountFrequency (%)
06101
73.3%
12
 
< 0.1%
26
 
0.1%
316
 
0.2%
472
 
0.9%
ValueCountFrequency (%)
57511
< 0.1%
57421
< 0.1%
57401
< 0.1%
57381
< 0.1%
57361
< 0.1%

model
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct78
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
iPhone12,1
1260 
iPhone11,8
1252 
iPhone9,1
563 
iPhone12,8
523 
iPhone8,1
 
386
Other values (73)
4343 

Length

Max length10
Median length10
Mean length9.367239102
Min length7

Characters and Unicode

Total characters78001
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowiPhone8,2
2nd rowiPhone12,1
3rd rowiPhone12,8
4th rowiPhone9,3
5th rowiPhone10,2
ValueCountFrequency (%)
iPhone12,11260
15.1%
iPhone11,81252
15.0%
iPhone9,1563
 
6.8%
iPhone12,8523
 
6.3%
iPhone8,1386
 
4.6%
iPhone10,2325
 
3.9%
iPhone10,1311
 
3.7%
iPhone12,5255
 
3.1%
iPhone9,2220
 
2.6%
iPhone10,4197
 
2.4%
Other values (68)3035
36.4%
2021-03-31T13:41:05.325352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
iphone12,11260
15.1%
iphone11,81252
15.0%
iphone9,1563
 
6.8%
iphone12,8523
 
6.3%
iphone8,1386
 
4.6%
iphone10,2325
 
3.9%
iphone10,1311
 
3.7%
iphone12,5255
 
3.1%
iphone9,2220
 
2.6%
iphone10,4197
 
2.4%
Other values (68)3035
36.4%

Most occurring characters

ValueCountFrequency (%)
110631
13.6%
i8327
10.7%
P8327
10.7%
,8327
10.7%
o7238
9.3%
h7004
9.0%
n7004
9.0%
e7004
9.0%
23347
 
4.3%
82449
 
3.1%
Other values (9)8343
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter38989
50.0%
Decimal Number22358
28.7%
Uppercase Letter8327
 
10.7%
Other Punctuation8327
 
10.7%

Most frequent character per category

ValueCountFrequency (%)
110631
47.5%
23347
 
15.0%
82449
 
11.0%
01247
 
5.6%
91194
 
5.3%
3837
 
3.7%
5786
 
3.5%
7759
 
3.4%
4582
 
2.6%
6526
 
2.4%
ValueCountFrequency (%)
i8327
21.4%
o7238
18.6%
h7004
18.0%
n7004
18.0%
e7004
18.0%
d1323
 
3.4%
a1089
 
2.8%
ValueCountFrequency (%)
P8327
100.0%
ValueCountFrequency (%)
,8327
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47316
60.7%
Common30685
39.3%

Most frequent character per script

ValueCountFrequency (%)
110631
34.6%
,8327
27.1%
23347
 
10.9%
82449
 
8.0%
01247
 
4.1%
91194
 
3.9%
3837
 
2.7%
5786
 
2.6%
7759
 
2.5%
4582
 
1.9%
ValueCountFrequency (%)
i8327
17.6%
P8327
17.6%
o7238
15.3%
h7004
14.8%
n7004
14.8%
e7004
14.8%
d1323
 
2.8%
a1089
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII78001
100.0%

Most frequent character per block

ValueCountFrequency (%)
110631
13.6%
i8327
10.7%
P8327
10.7%
,8327
10.7%
o7238
9.3%
h7004
9.0%
n7004
9.0%
e7004
9.0%
23347
 
4.3%
82449
 
3.1%
Other values (9)8343
10.7%

operating_system
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct53
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
iOS 14.2
4842 
iOS 14.1
884 
iOS 14.0.1
520 
iOS 13.7
 
331
iOS 14.0
 
309
Other values (48)
1441 

Length

Max length10
Median length8
Mean length8.390296625
Min length8

Characters and Unicode

Total characters69866
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowiOS 14.0.1
2nd rowiOS 14.2
3rd rowiOS 14.1
4th rowiOS 13.7
5th rowiOS 14.2
ValueCountFrequency (%)
iOS 14.24842
58.1%
iOS 14.1884
 
10.6%
iOS 14.0.1520
 
6.2%
iOS 13.7331
 
4.0%
iOS 14.0309
 
3.7%
iOS 13.6.1214
 
2.6%
iOS 13.5.1169
 
2.0%
iOS 12.4.9156
 
1.9%
iOS 14.2.1156
 
1.9%
iOS 13.6146
 
1.8%
Other values (43)600
 
7.2%
2021-03-31T13:41:05.513177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ios8327
50.0%
14.24842
29.1%
14.1884
 
5.3%
14.0.1520
 
3.1%
13.7331
 
2.0%
14.0309
 
1.9%
13.6.1214
 
1.3%
13.5.1169
 
1.0%
14.2.1156
 
0.9%
12.4.9156
 
0.9%
Other values (44)746
 
4.5%

Most occurring characters

ValueCountFrequency (%)
110547
15.1%
.9952
14.2%
i8327
11.9%
O8327
11.9%
S8327
11.9%
8327
11.9%
47182
10.3%
25375
7.7%
31491
 
2.1%
0890
 
1.3%
Other values (5)1121
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26606
38.1%
Uppercase Letter16654
23.8%
Other Punctuation9952
 
14.2%
Lowercase Letter8327
 
11.9%
Space Separator8327
 
11.9%

Most frequent character per category

ValueCountFrequency (%)
110547
39.6%
47182
27.0%
25375
20.2%
31491
 
5.6%
0890
 
3.3%
6366
 
1.4%
7333
 
1.3%
5190
 
0.7%
9156
 
0.6%
876
 
0.3%
ValueCountFrequency (%)
O8327
50.0%
S8327
50.0%
ValueCountFrequency (%)
i8327
100.0%
ValueCountFrequency (%)
8327
100.0%
ValueCountFrequency (%)
.9952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common44885
64.2%
Latin24981
35.8%

Most frequent character per script

ValueCountFrequency (%)
110547
23.5%
.9952
22.2%
8327
18.6%
47182
16.0%
25375
12.0%
31491
 
3.3%
0890
 
2.0%
6366
 
0.8%
7333
 
0.7%
5190
 
0.4%
Other values (2)232
 
0.5%
ValueCountFrequency (%)
i8327
33.3%
O8327
33.3%
S8327
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII69866
100.0%

Most frequent character per block

ValueCountFrequency (%)
110547
15.1%
.9952
14.2%
i8327
11.9%
O8327
11.9%
S8327
11.9%
8327
11.9%
47182
10.3%
25375
7.7%
31491
 
2.1%
0890
 
1.3%
Other values (5)1121
 
1.6%

mean_wifi_state
Real number (ℝ≥0)

ZEROS

Distinct1373
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7771132884
Minimum0
Maximum1
Zeros1181
Zeros (%)14.2%
Memory size65.2 KiB
2021-03-31T13:41:05.614211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6976744186
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3023255814

Descriptive statistics

Standard deviation0.3755285386
Coefficient of variation (CV)0.4832352557
Kurtosis0.06579014538
Mean0.7771132884
Median Absolute Deviation (MAD)0
Skewness-1.350939843
Sum6471.022352
Variance0.1410216833
MonotocityNot monotonic
2021-03-31T13:41:05.724589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15333
64.0%
01181
 
14.2%
0.613
 
0.2%
0.811
 
0.1%
0.818181818210
 
0.1%
0.49
 
0.1%
0.71428571438
 
0.1%
0.57142857148
 
0.1%
0.33333333338
 
0.1%
0.66666666677
 
0.1%
Other values (1363)1739
 
20.9%
ValueCountFrequency (%)
01181
14.2%
0.0054054054051
 
< 0.1%
0.0059171597631
 
< 0.1%
0.0086206896551
 
< 0.1%
0.010869565221
 
< 0.1%
ValueCountFrequency (%)
15333
64.0%
0.99834710741
 
< 0.1%
0.99810964081
 
< 0.1%
0.99743589741
 
< 0.1%
0.99740932641
 
< 0.1%

last_session_no
Real number (ℝ≥0)

Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.35102678
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Memory size65.2 KiB
2021-03-31T13:41:05.819089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum67
Range66
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.433926422
Coefficient of variation (CV)1.024738579
Kurtosis49.94285072
Mean3.35102678
Median Absolute Deviation (MAD)1
Skewness4.823208489
Sum27904
Variance11.79185068
MonotocityNot monotonic
2021-03-31T13:41:05.903681image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
12663
32.0%
21785
21.4%
31198
14.4%
4829
 
10.0%
5565
 
6.8%
6345
 
4.1%
7236
 
2.8%
8170
 
2.0%
9143
 
1.7%
1096
 
1.2%
Other values (29)297
 
3.6%
ValueCountFrequency (%)
12663
32.0%
21785
21.4%
31198
14.4%
4829
 
10.0%
5565
 
6.8%
ValueCountFrequency (%)
671
< 0.1%
591
< 0.1%
551
< 0.1%
531
< 0.1%
491
< 0.1%

last_level
Real number (ℝ≥0)

Distinct85
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.0142909
Minimum0
Maximum1010
Zeros74
Zeros (%)0.9%
Memory size65.2 KiB
2021-03-31T13:41:05.999896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q165
median67
Q3116
95-th percentile1004.7
Maximum1010
Range1010
Interquartile range (IQR)51

Descriptive statistics

Standard deviation394.5503538
Coefficient of variation (CV)1.389191905
Kurtosis-0.3746700747
Mean284.0142909
Median Absolute Deviation (MAD)2
Skewness1.268439355
Sum2364987
Variance155669.9817
MonotocityNot monotonic
2021-03-31T13:41:06.101809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
653230
38.8%
671070
 
12.8%
1002970
 
11.6%
116516
 
6.2%
70451
 
5.4%
1003351
 
4.2%
10255
 
3.1%
113247
 
3.0%
1004181
 
2.2%
1005135
 
1.6%
Other values (75)921
 
11.1%
ValueCountFrequency (%)
074
 
0.9%
4105
1.3%
51
 
< 0.1%
71
 
< 0.1%
10255
3.1%
ValueCountFrequency (%)
101053
0.6%
100960
0.7%
100824
 
0.3%
100752
0.6%
100693
1.1%

totalrevenue
Real number (ℝ≥0)

Distinct6992
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3589169169
Minimum0.0002
Maximum7.4779144
Zeros0
Zeros (%)0.0%
Memory size65.2 KiB
2021-03-31T13:41:06.198706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.0002
5-th percentile0.02623
Q10.1008922
median0.2346
Q30.4699714
95-th percentile1.1232756
Maximum7.4779144
Range7.4777144
Interquartile range (IQR)0.3690792

Descriptive statistics

Standard deviation0.3996731404
Coefficient of variation (CV)1.113553365
Kurtosis27.45675537
Mean0.3589169169
Median Absolute Deviation (MAD)0.157276
Skewness3.400794171
Sum2988.701167
Variance0.1597386192
MonotocityNot monotonic
2021-03-31T13:41:06.293734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.000452
 
0.6%
0.000848
 
0.6%
0.032428
 
0.3%
0.001225
 
0.3%
0.000624
 
0.3%
0.031623
 
0.3%
0.031220
 
0.2%
0.036215
 
0.2%
0.002414
 
0.2%
0.036612
 
0.1%
Other values (6982)8066
96.9%
ValueCountFrequency (%)
0.00028
0.1%
0.0002241
 
< 0.1%
0.00023121
 
< 0.1%
0.000251
 
< 0.1%
0.00025161
 
< 0.1%
ValueCountFrequency (%)
7.47791441
< 0.1%
6.15339561
< 0.1%
5.03998321
< 0.1%
4.64313321
< 0.1%
3.8257207981
< 0.1%

Interactions

2021-03-31T13:40:32.105636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.201528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.288359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.380603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.474148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.556607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.641661image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.722630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.809377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.894302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:32.982270image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.073117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.159396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.248883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.346030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.444973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.536442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.627324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.721482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.814594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:33.971211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.067651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.155197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.246718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.333177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.428339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.519175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.610160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.701054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.791087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.881672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:34.969328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.060603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.147728image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.238117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.320809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.408303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.495713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.586573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.667286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.751795image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.832340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:35.916293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.001154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.085519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.170368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.254850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.339107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.420651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.505736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.586750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.674876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.836183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:36.933097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.023499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.121770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.210865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.305110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.391975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.482567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.573451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.664946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.756085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.846410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:37.937235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.025215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.117868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.205656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.296439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.388532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.485214image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.581559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.700027image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.801789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.900672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:38.993690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.086411image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.180770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.289204image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.386859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.487162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.581798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.672491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.766895image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.857899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:39.953899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.037931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.125916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.207597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.292218image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.460875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.543640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.621244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.702158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.783176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.865075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:40.946617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.027391image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.108706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.187304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.269630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.347606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.430133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.515840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.607481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.695335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.785485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.879189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:41.964249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.046440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.135275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.235110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.325703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.413328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.499384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.587786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.671545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.758074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.841461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:42.927646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.007652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.091173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.170230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.253919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.339377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.422012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.511274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.593452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.673429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.757792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.841372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:43.927472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.011691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.094945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.178984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.256632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.350851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.446981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.536560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.735853image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.837481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:44.937502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.030127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.121601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.207776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.294723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.384001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.474263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.562884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.653375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.738649image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.824744image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.909455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:45.994776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:46.079983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:46.173068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:46.259708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:46.350920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:46.524454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:46.694521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:47.166132image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:49.909577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:50.248844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:50.342468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:50.441021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:50.540504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:50.633607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:50.919677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:51.013483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:51.112762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:51.210589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:51.311081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:51.502711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:51.599396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:40:51.688330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:55.729277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:55.913624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:40:59.950213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:41:00.054617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-03-31T13:41:00.456091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:41:00.556645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:41:00.650250image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-31T13:41:00.746463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-03-31T13:41:06.399793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-31T13:41:06.627421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-31T13:41:06.857147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-31T13:41:07.082685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-31T13:41:07.272865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-31T13:41:00.946085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-31T13:41:01.347245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

userID_anonymizedmean_revenue_banner_impressionmean_revenue_interstitial_impressionmean_revenue_level_eventmean_revenue_rewarded_impressionstd_revenue_banner_impressionstd_revenue_interstitial_impressionstd_revenue_level_eventstd_revenue_rewarded_impressiontotal_count_banner_impressiontotal_count_interstitial_impressiontotal_count_level_eventtotal_count_rewarded_impressionlast_time_banner_impressionlast_time_interstitial_impressionlast_time_level_eventlast_time_rewarded_impressionmodeloperating_systemmean_wifi_statelast_session_nolast_leveltotalrevenue
000081f8b-253d0.0004000.0300000.00.0000.0000000.0000000.00.0617013.02.014.00.0iPhone8,2iOS 14.0.11.0000003650.032400
1000ab112-26160.0003750.0300000.00.0000.0003180.0000000.00.021200.00.00.00.0iPhone12,1iOS 14.21.000000140.030750
2002ff54d-87490.0006690.0275170.00.0400.0009620.0055410.00.0118307624078.04076.04078.01621.0iPhone12,8iOS 14.10.814159810041.211006
30030d0af-28300.0004160.0229200.00.0400.0001840.0051290.00.082256015137.05136.05137.0763.0iPhone9,3iOS 13.71.000000510030.881707
400441340-5d9c0.0005370.0300000.00.0000.0000960.0035360.00.01651802368.02035.02369.00.0iPhone10,2iOS 14.21.0000004670.184700
500549864-65700.0003610.0256760.00.0450.0001200.0062270.00.0591742158.057.058.08.0iPhone9,4iOS 14.21.000000210020.502777
6006707fd-b6ec0.0004060.0306030.00.0000.0000260.0068450.00.02061907.07.07.00.0iPhone11,8iOS 14.21.0000001670.191733
700751678-39560.0004820.0270830.00.0000.0001710.0033230.00.02562005352.05352.05352.00.0iPhone11,8iOS 14.20.8627457650.174768
8007950b4-6beb0.0001840.0270000.00.0000.0000530.0041080.00.01451401682.073.01682.00.0iPhone12,1iOS 14.11.0000002650.137579
900807dbc-f7bf0.0003670.0250000.00.0000.0000520.0000000.00.06140975.02.0975.00.0iPhone13,1iOS 14.10.0000002220.027200

Last rows

userID_anonymizedmean_revenue_banner_impressionmean_revenue_interstitial_impressionmean_revenue_level_eventmean_revenue_rewarded_impressionstd_revenue_banner_impressionstd_revenue_interstitial_impressionstd_revenue_level_eventstd_revenue_rewarded_impressiontotal_count_banner_impressiontotal_count_interstitial_impressiontotal_count_level_eventtotal_count_rewarded_impressionlast_time_banner_impressionlast_time_interstitial_impressionlast_time_level_eventlast_time_rewarded_impressionmodeloperating_systemmean_wifi_statelast_session_nolast_leveltotalrevenue
8317ffc2d2a8-f1500.0006110.0275420.00.000.0005500.0050250.00.0541219041.041.041.00.0iPhone8,1iOS 11.4.11.0000002670.363500
8318ffc53521-d36a0.0001990.0242780.00.040.0000580.0111440.00.025928182.082.082.05.0iPhone10,3iOS 14.20.000000210020.263469
8319ffc97ffe-6d800.0003910.0300000.00.000.0002030.0133850.00.0124120579.0579.0579.00.0iPhone8,1iOS 14.21.0000003650.124694
8320ffcc3117-6a520.0005000.0350000.00.000.0001300.0086600.00.01457804490.0389.04490.00.0iPhone12,5iOS 14.00.00000081000.182000
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